"Common Lisp" matching MCP servers:

Automatically provides AI agents with proven instructions and past failure warnings for common tasks like deployment, auth, and payments, enabling flawless execution without manual configuration.

Enables multiple AI agents to share an append-only context store where they checkpoint work, record failures, claim files, hand off tasks, and recover interrupted sessions. It provides a single-file, multi-process-safe server so the next agent can continue where the last one stopped.

A home for any agent on the internet: inbox, public memory, job board, rooms, human help. Free, no key.

Provides access to personal/team knowledge repositories with tools to search, retrieve, and save information from various sources including Slack, Gmail, and document storage platforms.

Enables multiple AI agents to share, incrementally synchronize, and restore work context within a common scope using persistent tickets, immutable updates, and text artifacts.

MCP server that helps coding LLMs avoid re-solving common problems by serving verified minimal code samples and compatibility evidence, with tools to search, retrieve, and explain known solutions across languages and environments.

A simple, beginner-friendly Model Context Protocol (MCP) server written in Python. It lets any MCP-compatible AI client (like Claude Desktop) manage a to-do list on your computer through five tools: add, view, get by ID, complete, and delete.

Enables AI agents to search verified collective knowledge, request and fulfill cognition bounties, and contribute to immune defense through MCP tools.

Enables multiple AI agents to share persistent memory, interact through a game hall and AI lounge, and bridge messages to ChatGPT and Claude browser sessions via MCP tools.

Enables AI agents to create, search, and manage subject matter expert profiles, including private workspace organization and moderated sharing to a common library.

Lets coding agents like Codex, Cursor, and Claude Code share and resume each other's chats, and attach to a common task with saved notes that can be searched later. This keeps task state and decisions in sync no matter which agent you switch to.

Enables accessing and managing personal/team internal knowledge repository with tools for semantic search, smart search, document listing, and saving information for future recall.

Enables agents to search, read, publish, and report reuse of problem-solving solutions via MCP.

Coordination for parallel coding agents: TTL file claims stored in the git common dir (visible across all worktrees), enforcement hooks that block colliding edits, agent presence, handoff notes, and a git-committed lessons knowledge base with BM25 search. Single static Go binary — no server, no database.

Enables several Claude windows or accounts to save and search a common set of notes, preferences, and decisions, and to leave topic-filed messages for each other that the recipient reads on demand. All data stays in two local JSONL files with no network access, so multiple instances can share context and hand off work without repeating background.

Enables software agents to maintain a signed, evidence-aware local ledger of claims, with policy-gated proposals, conflict queries, and constitution-pinned access through an MCP stdio server.

Lets multiple AI agents and parallel coding sessions share one memory and task ledger, so they can claim work instead of duplicating it, resume each other's handoffs and reasoning, and use credentials without ever seeing the keys. Connects assistants such as Claude Code, Codex, Cursor, Gemini, ChatGPT and custom agents over MCP, backed by a single local SQLite file or a self-hosted Postgres hub.

Provides a unified block-spindle-pscale function for reading, writing, and managing locks on JSON blocks across federated beaches, sed collectives, and bilateral grains.

Enables agents to retrieve, submit, critique, and verify structured knowledge records with provenance, uncertainty, and references over a public federated HTTP/MCP layer.

Serves one shared knowledge store over MCP so multiple coding agents can read and write the same facts, standing rules and research notes, with duplicate detection, per-repository scopes and inheritance from a global scope. This lets a correction taught to one agent apply to all of them at once, while tracking usage and keeping concurrent writes safe.

Enables AI agents to persist and retrieve structured thinking graphs using SQLite-backed memory with support for CRUD operations, graph search, and path finding.